Jul 2026· Artificial Intelligence and Applications· 0 citations· 51 references
TL;DR
This review presents a comprehensive taxonomy of post-2017 architectures, a comparative synthesis of laboratory versus deployment performance, and a critical analysis of key implementation challenges, and outlines key recommendations for developing adaptive, location-independent models.
Abstract
WiFi-based human activity recognition (HAR) and fall detection have emerged as promising alternatives to vision- and wearable-based systems for modern assisted living and clinical monitoring for the elderly. These approaches exploit variations in Channel State Information, capturing amplitude and phase fluctuations across subcarriers to characterize how human motion perturbs these patterns through multipath propagation. This narrative review synthesizes 26 peer-reviewed studies published between 2017 and 2025, focusing on systems evaluated in real-world, non-laboratory environments. While recent deep learning models demonstrate near-perfect benchmark accuracy on fine-grained datasets, cross-environment assessments consistently reveal a severe “reality gap.” Specifically, several reviewed studies reported accuracy declines exceeding 20% when deployed in previously unseen buildings, while multi-user interference reduced recognition accuracy by 21–46%. Furthermore, dataset availability and reproducibility are critical considerations; only a limited number of reviewed studies utilized publicly available datasets, and none provided a standardized benchmarking guide-line. To address these gaps, this review presents a comprehensive taxonomy of post-2017 architectures, a comparative synthesis of laboratory versus deployment performance, and a critical analysis of key implementation challenges. We conclude by proposing a set of recommended reporting practices and outlining actionable future research. In doing so, the paper outlines key recommendations for developing adaptive, location-independent models, emphasizing the need for domain-transfer techniques, cross-building validation, and expanding open datasets to enhance generalizability.
Received: 31 August 2025 | Revised: 13 March 2026 | Accepted: 25 June 2026
Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.
Data Availability Statement
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Author Contribution Statement
Kok Chung Chua: Conceptualization, Methodology, Validation, Formal analysis, Writing – original draft, Writing – review & editing, Visualization. Kai Liang Lew: Resources, Writing – original draft, Writing – review & editing, Project administration. Chean Khim Toa: Conceptualization, Validation, Resources, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Matthew Alexander Paudianto: Investigation, Data Curation, Writing– original draft, Writing – review & editing. Ivan Nathanael: Investigation, Data curation, Writing – original draft, Writing – review & editing. Abel Nathanael Hutapea: Investigation, Data curation, Writing – original draft, Writing – review & editing.
Driven by the rising demand for privacy-friendly, unobtrusive indoor monitoring, Wi-Fi passive human sensing has developed into a widely studied technical paradigm. Compared with vision-based and wearable sensing techniques, Wi-Fi sensing exhibits unique advantages including robust illumination, privacy protection, wall penetration, device-free operation, and low hardware cost. Driven by advanced deep learning, this field has advanced substantially over the past decade, evolving from coarse-level activity classification to fine-grained tasks including 3D human skeleton estimation, dense body pose matching, and multi-user activity sensing. However, several fundamental bottlenecks remain unresolved, including severe performance degradation in cross-domain and cross-environment scenarios, insufficient spatial resolution for motion sensing, limited capability for multi-user signal disentanglement, and the lack of standardized benchmark datasets. This survey provides a systematic overview of cutting-edge research on Wi-Fi-enabled indoor human activity detection. We classify mainstream technologies along three dimensions: signal processing pipelines, learning paradigms, and application granularity, and further dissect core challenges including environmental adaptability, data scarcity, and system scalability. Finally, we highlight promising future research directions covering domain generalization, cross-modal foundation model distillation, multi-user collaborative sensing, and real-time on-device deployment.
Zengqian Song, Jingming Li· Journal of Advances in Engin...· 0 citations
Accidental falls among the elderly demand highly reliable detection systems; however, existing solutions based on wearables, cameras, or WiFi often suffer from environmental interference, privacy concerns, and poor performance in detecting slow-onset falls (e.g., fainting). Despite these challenges, millimeter-wave (mmWave) radar-based methods have emerged as an attractive alternative. In this article, we propose an effective and efficient fall detection method, mmFallBbox, which leverages mmWave radar to track 3-D human bounding box dynamics. By exploiting the correlation between fall states and bounding box evolution, our approach effectively distinguishes between normal activities and slow fall states, such as fainting or medical conditions, which traditional systems struggle to detect. To evaluate the performance of mmFallBbox, we collected a large-scale fall detection dataset consisting of 60 h of radar data synchronized with video annotations. This dataset will be made publicly available to the research community for further development. We achieved an $F1$ score of 0.977 on our dataset and achieved state-of-the-art performance with limited computational complexity. Moreover, extensive experimental results show significant improvements in detecting slow-onset falls and providing explainable outputs, offering a promising solution for real-world fall detection applications.
Wenxuan Li, Dongheng Zhang, Jianwen Tong et al.· IEEE Transactions on Radar S...· 0 citations
A novel lightweight cross-domain few-shot sensor-based HAR network (CFSH-Net) is proposed for cross-domain activity recognition with limited labeled samples, which demonstrates strong cross-user generalization on PAMAP2 and USC-HAD, and stable cross-dataset transfer when trained on OPPORTUNITY and evaluated on four other datasets.
Hao Zheng, Hongji Xu, Fei Gao et al.· IEEE journal of biomedical a...· 0 citations
Wi-Fi Channel State Information (CSI) is a robust, privacy-preserving modality for Human Activity Recognition (HAR). Since raw CSI suffers from hardware desynchronizations and noise, preprocessing is vital. This study conducts an empirical ablation of CSI preprocessing using a fixed Two-Stream 2D CNN(Convolutional Neural Network) to quantify its impact on classification accuracy and latency. Results reveal that preprocessing, apart from architectural complexity, is the primary driver of the accuracy-latency trade-off. Computationally heavy methods like Hampel filtering introduce massive latency (>162 ms) without accuracy gains. In contrast, lightweight frequency-domain filtering consistently yields superior results. Specifically, dual-stream Butterworth bandpass filtering achieves 96.43% accuracy with only 49.08 ms latency. These findings demonstrate that isolating motion-relevant frequencies enables efficient, high-performance HAR suitable for real-time edge deployment.
Yiğit Can Çelik, Erdem Bera Emiroğlu, Sajjad Baghaee et al.· Signal Processing and Commun...· 0 citations
Human Activity Recognition (HAR) is a fast-growing research area that focuses on identifying human actions using data collected from sensors and vision-based devices. It plays an important role in applications like health monitoring, smart homes, surveillance, sports analysis, and human-computer interaction. In recent years, several methods have been developed to improve the performance of HAR systems using machine learning, deep learning, and hybrid models. This paper presents a detailed review of different methods used in HAR. The study is divided into three main categories: vision-based methods, sensor-based methods, and hybrid approaches that combine both types. Each method is discussed with examples from recent research, along with their advantages and limitations. A comparison is also provided in the form of a table to highlight the performance and challenges of each approach. Although HAR systems have achieved good results in controlled environments, several challenges still remain. These include poor generalization to new users or unknown environments, difficulty in recognizing complex or overlapping activities, dependence on large datasets, and lack of real-time performance. This paper also discusses these research gaps based on recent findings. The future of HAR depends on building more accurate, reliable, and real-time systems that can adapt to different situations. The paper concludes by suggesting possible directions for future work, such as the development of lightweight models, use of standard datasets, better handling of real-time data, and making models more interpretable.
